Crop classification and growth tracking with synthetic aperture radar

ABSTRACT

A computer-implemented method executed by one or more satellites for assessing crop development by using synthetic aperture radar (SAR) is presented. The method includes generating SAR images from scanning fields including crops, monitoring grown of the crops within the fields during a predetermined time period, and estimating a height of the crops during the predetermined time period by using interferometric information from one or more of the SAR images and tracking change in height and growth rates. The method further includes differentiating between crops in different fields by monitoring changes in the height of the crops during an entire growing season.

BACKGROUND Technical Field

The present invention relates generally to assessing crop development,and more specifically, to assessing crop development by using syntheticaperture radar (SAR) images.

Description of the Related Art

Synthetic Aperture Radars (SARs) transmit and receive energy atmicrowave frequencies. A response recorded by these sensors is largely afunction of the structure and dielectric properties of a target. Thestructure of a canopy is different among crops, and changes as cropsgrow. SARs respond well to these structural differences and thus thesesensors are able to accurately identify crop type and have provensensitive to several crop biophysical parameters. Although opticalsensors have traditionally been used for crop monitoring, advances inSAR applications research coupled with availability of SAR data atdifferent frequencies and polarizations has raised the profile of thesesensors for agricultural monitoring. The “all weather” capability ofSARs makes their use in operational activities of particular interest.Advancements in SAR applications development, continued improved accessto data, and a push to transfer SAR research methods to monitoringagencies has led to an increased role of SAR in monitoring agriculturalproduction.

SUMMARY

In accordance with one embodiment, a computer-implemented methodexecuted by one or more satellites for assessing crop development byusing a synthetic aperture radar (SAR) is provided. Thecomputer-implemented method includes generating SAR images from scanningfields including crops, monitoring grown of the crops within the fieldsduring a predetermined time period, and estimating a height of the cropsduring the predetermined time period by using interferometricinformation from one or more SAR images and tracking change in heightand growth rates.

In accordance with another embodiment, a system for assessing cropdevelopment by using a synthetic aperture radar (SAR) is provided. Thesystem includes one or more satellites for generating SAR images fromscanning fields including well established structures where the locationand height of the object is well established, these points being calledanchor points. The signal reflected from the anchor points and thesignal reflected from crops provide a change that can quantify theheight of the crops, a SAR processing system for measuring the absoluteheight of the crops at the moment the SAR image is acquired and trackinggrown of the crops within the fields during a predetermined time period,and crop growth models for estimating a height of the crops during thepredetermined time period.

In accordance with another embodiment, a system for assessing cropdevelopment by using a synthetic aperture radar (SAR) is provided. Thesystem includes one or more satellites for generating SAR images fromscanning fields including crops, a SAR processing system for monitoringgrown of the crops within the fields during a predetermined time period,and crop growth models for estimating a height of the crops during thepredetermined time period.

In accordance with one embodiment, a system for assessing cropdevelopment by using a synthetic aperture radar (SAR) is provided. Thesystem includes one or more moving aerial objects for generating SARimages from scanning a plurality of fields including different crops, aSAR processing system for monitoring grown of the different crops withinthe plurality of fields during a growing season, and crop growth modelsfor estimating a height of the different crops during the growth seasonand continuously comparing heights of the different crops during thegrowing season.

Furthermore, embodiments can take the form of a related computer programproduct, accessible from a computer-usable or computer-readable mediumproviding program code for use, by or in connection with a computer orany instruction execution system. For the purpose of this description, acomputer-usable or computer-readable medium may be any apparatus thatmay include means for storing, communicating, propagating ortransporting the program for use, by or in a connection with theinstruction execution system, apparatus, or device.

These and other features and advantages will become apparent from thefollowing detailed description of illustrative embodiments thereof,which is to be read in connection with the accompanying drawings.

BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWINGS

The invention will provide details in the following description ofpreferred embodiments with reference to the following figures wherein:

FIG. 1 is a synthetic aperture radar (SAR) image acquisition system foracquiring images of crops, in accordance with an embodiment of thepresent invention;

FIG. 2 is a block/flow diagram of an exemplary satellite SAR viewinggeometry and spatial resolution system, in accordance with an embodimentof the present invention;

FIG. 3 is a block/flow diagram of an exemplary SAR system depicting areceiver, transceiver, and processing system, in accordance with anembodiment of the present invention;

FIG. 4 is a block/flow diagram of an exemplary SAR crop image processingsystem, in accordance with an embodiment of the present invention;

FIG. 5 is a block/flow diagram of an exemplary system for integratingcrop models, maps derived from SAR, earth observations data processing,and in-situ observation data to a geo portal capable of being accessedby end-users, in accordance with an embodiment of the present invention;

FIG. 6 is a block/flow diagram illustrating monitoring of crops viasatellites generating SAR images and sending the SAR images forprocessing, in accordance with an embodiment of the present invention;

FIG. 7 is a block/flow diagram illustrating monitoring of differentcrops from different fields via satellites generating SAR images forprocessing, in accordance with an embodiment of the present invention;and

FIG. 8 is a block/flow diagram illustrating estimation of rate of growthfor different crops in different fields, in accordance with anembodiment of the present invention.

Throughout the drawings, same or similar reference numerals representthe same or similar elements.

DETAILED DESCRIPTION

Embodiments in accordance with the present invention provide methods anddevices for employing geophysical exploration, which encompassesnumerous methods for better understanding terrestrial and marineenvironments. These methods are often applicable to agriculturalmanagement. One of the most promising geophysical exploration tools foragriculture includes the use of remote-sensing. The use of remotesensing from air and space borne sensors is desired. For many yearsaerial photogrammetry has been applied to several components of theagricultural production scheme and its management. Many systemscurrently use drones at the farm level but drone imaging is limited bythe capability of the system to cover large areas and estimates, forexample, the crop production across a continental scale. Satelliteimages, on the other hand, have the ability to cover large areas but arelimited by cloud-free images that are required for analysis. However,the ability to use multispectral sensors capturing emitted and reflectedlight beyond that visible to the naked eye and developing crop modelsrelated to crop growth and estimation methods for crop growth will bediscussed herein with respect to the exemplary embodiments of thepresent invention.

Embodiments in accordance with the present invention provide methods anddevices for employing a crop classification and crop growth trackingsystem. The crop classification and crop growth tracking system usessynthetic aperture radar (SAR). SAR can penetrate through clouds andacquire images independent of weather conditions. The reflected radarsignal from the ground is dependent on multiple factors, such as soilcomposition, soil moisture conditions, and vegetation density or generalinfrastructures like farm buildings, ponds etc. Plants with denserleaves and biomass will have a stronger reflected signal and this can beemployed to distinguish between plants that have higher or lower leafdensity. Such reflected radar signals, at the beginning of the growingseason can be good indicators or areas of lands that were planted, cantrack when plants are emerging from soil, and can monitor plant growthand track the density of the leaf. Further, radar image acquisitionunder different viewing angles can be used to interferometricallyreconstruct plant heights and track these plant height changes duringthe entire growing season. Change in plant heights is one way todifferentiate between crops, such as corn and soybean. Additionally, thesignal reflected by the crops is dependent on the leaf density.Analyzing the texture of the signal (noise associated with reflectedradar signal) is another differentiator signal to distinguish betweencrops.

Embodiments in accordance with the present invention provide methods anddevices for implementing SAR to estimate rate of growth for variouscrops and determine heights of crops during an entire growing season.SAR images include information about an amplitude and a phase of asignal acquired by geo-orbiting satellites. To detect changes in height,phase differences between images acquired at different moments of timeare compared. Furthermore, it is relevant to have well-defined anchorpoints that can provide a fixed signal reflectance that can bereferenced to the crop signal. For example, buildings with well-knownheights or other large infrastructures that are within the SAR imagesprovide the fixed point with respect to which a change in phase signalcan be extracted that can provide a measurement of the height of thecrops.

Referencing to a fixed height makes height change measurements betweentwo points less prone to noise. The height of houses, barns, storagetanks, etc. extracted from additional geospatial layers can be used toidentify signal from well-known height points. In many cases, resolutionof images on the Earth's surface can be in the range of meters. However,the phase signal due to change in height can be significantly smaller. Aprerequisite of image acquisition is an accurate knowledge of bareground topography of that location that constitutes the base ground. Thebare ground can be a SAR image acquired when the ground is not coveredby vegetation or can be a topographical image acquired using lightdetection and ranging (LIDAR) where surface structures are extracted andonly the bare topography is recovered. Bare ground can also beidentified from optical satellite images that show no signature ofvegetation across a growing season or reference can be made to, e.g.,paved roads.

Embodiments in accordance with the present invention thus providemethods and devices for employing well-known structure heights (e.g.,houses, barns, storage tanks, etc.), paved roads, or bare landtopographical datasets to detect tiny changes above the surface with aresolution of about less than 10 cm. Such changes can enable crop growthidentification specifically to detect crops that have significant heightdifferences, such as corn and soybean. The change in height of thesecrops is well established and correlates with weather data and growingpractices. By taking two measurements of the height of well-definedcrops, the growth rate can be established as the difference in heightdivided by the time lapse between the two-image acquisition. Taking intoaccount a crop growth model, the rate of growth for various crops can bequickly and accurately estimated and compared with the SAR extractedgrowth rate. From the average height difference between crops, eachfield can be identified as being planted with a specific crop whereareas with similar characteristics can be grouped together to define theextent of a plot that shares similar growth rates and plant height.Furthermore, by analyzing the reflected signal from the land, the noiseand spectral distribution of the signal can be a good indicator of theground features. Combining the signal texture and the growth rate canfurther improve the delineation of the areas that share similarcharacteristics. The changes in crop growth can also be an indicator ofvegetation state development.

The acquired SAR signal can be used as an independent source of signalor the SAR signal can be combined with optical signals from drones orsatellites to further enhance a precision of crop identification andgrowth rate. It is noted that due to spatial resolution mismatchesbetween different imaging signals, it will be understood that images canbe further decomposed based on spectral or texture information to highergranularity to improve crop identification.

It is to be understood that the present invention will be described interms of a given illustrative architecture; however, otherarchitectures, structures, substrate materials and process features andsteps/blocks can be varied within the scope of the present invention. Itshould be noted that certain features cannot be shown in all figures forthe sake of clarity. This is not intended to be interpreted as alimitation of any particular embodiment, or illustration, or scope ofthe claims.

Crop is defined as an “aggregation of individual plant species grown ina unit area for economic purpose.”

Growth is defined as an “irreversible increase in size and volume and isthe consequence of differentiation and distribution occurring in theplant.”

Simulation is defined as “reproducing the essence of a system withoutreproducing the system itself.” In simulation the essentialcharacteristics of the system are reproduced in a model, which is thenstudied in an abbreviated time scale.

A model is a schematic representation of the conception of a system oran act of mimicry or a set of equations, which represents the behaviorof a system. Also, a model is “a representation of an object, system oridea in some form other than that of the entity itself”. The purpose ofthe model is usually to aid in explaining, understanding or improvingperformance of a system. A model is, by definition, “a simplifiedversion of a part of reality, not a one to one copy.” Thissimplification makes models useful because it offers a comprehensivedescription of a problem situation.

Agricultural models are mathematical equations that represent thereactions that occur within the plant and the interactions between theplant and its environment. The model simulates or imitates the behaviorof a real crop by predicting the growth of its components, such asleaves, roots, stems, and grains. Thus, a crop growth model not onlypredicts the final state of total biomass or harvestable yield, but alsoincludes quantitative information about major processes involved in thegrowth and development of a plant.

Referring now to the drawings in which like numerals represent the sameor similar elements and initially to FIG. 1, a synthetic aperture radar(SAR) image acquisition system for acquiring images of crops ispresented, in accordance with an embodiment of the present invention.

The SAR image acquisition system 10 includes a plurality of satellites12 that generate SAR images from scanning crops 16 located on the ground14. The following equation can be used to generate the SAR images:

${\Delta\vartheta} = {{{- \frac{4\pi}{\lambda}}\frac{B_{n}}{R\; {\sin (\theta)}}} - {\frac{4\pi}{\lambda}\frac{B_{n}d}{R\; {\tan (\theta)}}}}$

Where λ is the radar wavelength, d is the change in height relative to atopographic height, B is a distance between satellites, R is a radius ofthe satellite 12 relative to ground 14, and 0 is the azimuth angle.

In this case, accurate knowledge of the topography can improve theheight change for a crop. In the SAR imagery, the polarization of theincident radar signal plays a significant role in the conversion of thereflected signal into the association of the signal with vegetativestate of the plant. Specifically, the circular polarized signal canprovide a better assessment of the signal coming from the vegetation.

In the proposed approach, a Landsat/Sentinel image can be employed toassess—that no vegetation is present when a SAR image is acquired. TheLandsat program is the longest running enterprise for acquisition ofsatellite imagery of Earth, running from 1972. The most recent, Landsat8, was launched on Feb. 11, 2013. The images are a unique resource forglobal change research and applications in agriculture, cartography,geology, forestry, regional planning, surveillance, and education.Landsat 8 data has eight spectral bands with spatial resolutions rangingfrom about 15 to about 60 meters and the temporal resolution is about 16days. The Sentinel is an Earth observation mission developed by theEuropean Space Agency (ESA) as part of the Copernicus Programme toperform terrestrial observations in support of services such as forestmonitoring, land cover changes detection, and natural disastermanagement. It includes a synthetic aperture radar satellite Sentinell-1and two identical optical satellites, Sentinel-2A and Sentinel-2B.

The spectral information received from the optical satellite can be usedto identify the no vegetation areas (or bare land images) and can beused as reference SAR images that can be assumed to be the bare landimage. Additionally, a noise-free reflection area in the SAR images is agood indicator of bare ground and can be confirmed using additionalgeospatial layers like topography or land use data. Thus, the SAR imagescan be supplemented with at least topography data, optical images,surveys, and farmer assessments to verify and improve estimations of theheight of the crops.

A second SAR image is acquired when the crop has emerged in thespringtime. A differential phase image is created to assess change inthe interferometry signal. The image is corrected for localtopographical variation to access the change in vegetation. The changein height for the crop is tracked multiple times after planting (duringa predetermined time period). The change is associated with grow ratefor a crop that is calculated as the height difference between twoconsecutive images divided by a time interval between satellite imageacquisition.

FIG. 2 is a block/flow diagram of an exemplary satellite SAR viewinggeometry and spatial resolution system, in accordance with an embodimentof the present invention.

The space-based, strip-map, monostatic SAR system 20 depicts a satellite12 travelling along a satellite path 15, which is a set of positions atwhich the SAR transmits a pulse 13. The satellite path 15 is along theazimuth direction 24. Each pulse 13 travels to a target area 30 wherethe antenna beam intercepts the earth and illuminates targets (e.g.,crops) at that location, and the reflected return pulses are in turncollected by the same antenna or satellite 12. The pulse 13 travelsalong a direction 26 to cover the entire target 30. The area covered orradar swath 22 is further illustrated. A first point 32 of the pulse 13is shown at a beginning region of the radar swath 22 and a second point34 of the pulse 13 is shown at an end region of the radar swath 22.

SAR works because the radar pulse 13 travels to and from the target 30at the speed of light, which is much faster than the speed of thesatellite 12. The radar pulse 13 can be translated into a graph 35depicting the measured radar signal 36. The measured radar signal 36 candesignate a height of a crop (e.g., corn) across a field. The height ofthe crop is designated on the y-axis and the a length of a field isdesignated on the x-axis.

In a SAR signal processor (FIG. 3), there are specific operations neededto convert a raw data set into an interpretable image 35. The raw SARdata is not an image since point targets are spread out in range and inthe azimuth direction 24. As the radar pulse 13 moves along the target30, the radar-to-target range varies, thus forming the curved trace 35.This translation also produces an along-track frequency or time trace inthe azimuth direction 24, induced by Doppler, and range pulse encodingproduces a somewhat similar time or frequency trace in range. The SARimage processor (FIG. 3) then compresses this distributed targetinformation in 2D to create the image 35.

To create a SAR image, successive pulses 13 of radio waves aretransmitted to “illuminate” the target scene 30, and the echo of eachpulse is received and recorded. The pulses 13 are transmitted and theechoes received using a satellite 12, with wavelengths of a meter downto several millimeters. As the SAR device on board the satellite 12moves, the antenna location relative to the target changes with time.Signal processing of the successive recorded radar echoes allows thecombining of the recordings from these multiple satellite positions.This process forms the SAR and allows the creation of higher-resolutionimages than would otherwise be possible.

The SAR system 20 saves the phase histories of the response at eachposition as the real beam or pulse 13 moves through the radar swath 22and then weighs, phase shifts, and sums the pulses 13 to focus on onepoint target at a time and suppress all others. The SAR system 20performs the weighing, shifting, and summing to focus on each pointtarget in turn. The SAR system 20 then constructs an image by placingthe total energy response obtained in the focusing on a particulartarget (e.g., crop) at the position in the image corresponding to thattarget. SAR achieves a very high signal processing gain because ofcoherent (in-phase) summation of the range-correlated responses in theradar.

SAR is a form of radar that is used to create 2D or 3D images ofobjects, such as landscapes or crop fields. SAR uses the motion of theradar antenna over a target region to provide finer spatial resolutionthan conventional beam-scanning radars. SAR is usually mounted on amoving platform, such as an aircraft or spacecraft or satellites, andSAR has its origins in an advanced form of side looking airborne radar(SLAR). The distance the SAR device travels over a target (i.e., cropfield) in the time taken for the radar pulses 13 to return to theantenna or satellite 12 creates the large synthetic antenna aperture(the size of the antenna). Typically, the larger the aperture, thehigher the image resolution will be, regardless of whether the apertureis physical (a large antenna) or synthetic (a moving antenna). Thisallows SAR to create high-resolution images with comparatively smallphysical antennas.

The 3D processing is performed in two steps: the azimuth direction 24and range direction 26 are focused for the generation of 2D(azimuth-range) high-resolution images, after which a digital elevationmodel (DEM) is used to measure the phase differences between compleximages, which is determined from different look angles to recover theheight information. This height information, along with theazimuth-range coordinates provided by 2D SAR focusing, gives the thirddimension, which is the elevation direction. The first step needs onlystandard processing algorithms and for the second step, an additionalpre-processing stage such as image co-registration and phase calibrationis used. In addition to this, multiple baselines can be used to extend3D imaging to the time dimension. 4D and multi-D SAR imaging allowsimaging of complex scenarios, such as fields including crops, and hasimproved performances with respect to classical interferometrictechniques such as persistent scatterers interferometry (PSI).

FIG. 3 is a block/flow diagram of an exemplary SAR system depicting areceiver, transceiver, and processing system, in accordance with anembodiment of the present invention.

As noted above, SAR is an imaging radar mounted on a moving platform.Electromagnetic waves are sequentially transmitted, and reflected echoesare collected, digitized, and stored by the radar antenna for laterprocessing. As transmission and reception occur at different times, theymap to different positions. The well-ordered combination of the receivedsignals builds a virtual aperture that is much longer than the physicalantenna length. This is why it is named “synthetic aperture,” giving itthe property of being an imaging radar. The range direction is parallelto flight track and perpendicular to azimuth direction, which is alsoknown as along-track direction because it is in line with the positionof the object within the antenna's field of view.

In various exemplary embodiments, the SAR system 40 includes atransmitter 50, a receiver 60, and a SAR processing computer system 70.

The transmitter 50 includes a waveform generator 52 and a poweramplifier 54. The transmitter 50 communicates with the receiver 60 via atransmitter/receiver switch or T/R switch 42. The T/R switch 42 receivesSAR images from satellite 12 which generates the data from scanning atarget 44 (e.g., field of crops).

The receiver 60 sends the SAR data to the SAR processing computer system70. The SAR processing computer system 70 includes an A/D converter 72,a pulse processing block 74, a motion compensation block 76, an imageformation block 78, and a pixel processing block 80. The processed datais then sent to computer processing units 90 and data recording units92. The computer processing units 90 can be computers, lap tops, smartphones, tablets, a personal digital assistant, a wearable device, anInternet appliance, a communications device, wired or wireless device,or any other electronic device having at least one processor forprocessing the SAR images. The recording units 92 can be any type ofmemory.

Thus, a SAR includes an end-to-end system that has radar building blockssuch as an antenna or satellite 12, a transmitter 50, a receiver 60, anda high technology data processing system 70. The radar maintainsstringent control of signal characteristics and collects coherent phaseinformation to allow for the construction of the image. Ifphase-preserving disciplines are rigidly enforced, a SAR can produce animage whose along-track spatial resolution is largely independent ofwavelength and target range. As a result, there must be a translation ofeither the target through the real radar beam, or the real beam throughthe target, or a combination of both processes that produce thenecessary systematic change of phase in the target's signal during theobservation time of the radar

FIG. 4 is a block/flow diagram of an exemplary SAR crop image processingsystem, in accordance with an embodiment of the present invention.

In various exemplary embodiments, the SAR crop image processing systemincludes a backscattering measuring and parameter sampling block 102that communicates backscattering coefficients 104 and SAR images 106 toa SAR image processing block 108. The SAR data then goes through aspeckle filtering block 110. The filtered SAR data is then classifiedinto a plurality of different classes by a classified SAR image block112.

Regarding the speckle filtering block 110, it is noted that SARtransmits a precise signal toward its target (e.g., crop field) and,when the reflected radiation returns, a SAR records not only theamplitude of that signal, but its phase as well. It is the phaseinformation which allows for the post-facto coherent summation ofthousands of recorded signals in the correlator during the aperturesynthesis operation. Coherent signals have properties that areconsiderably different from their non-coherent counterparts. Thecoherent interference between targets included within a resolution cellis the basis for much of the scintillation of coherent radar imagery, aneffect referred to as speckle. Thus, there is a wide variation in theSAR image, even when given a uniform input. This variation can beconsidered as form of noise. This speckle can be filtered by the specklefiltering block 110.

Moreover, the classified SAR images can be combined with crop map dataand biomass data via the crop map block 120 and the biomass map 122. Theclassified SAR images go through backscattering extraction block 114 andare provided to a crop models block 116. The crop models block 116includes creating, training, and updating crop models for determining atleast crop growth rates of a plurality of different plants in aplurality of different fields. The crop models block 116 can alsoreceive and process water and cloud semi-empirical data models 118. Thebackscattering measuring and parameter sampling block 102 furthergenerates crop growth parameters via a crop growth parameter block 124and combines the data with the biomass map 122.

Therefore, FIG. 4 illustrates one process for forming crop models 116that can at least estimate crop growth rates across a plurality offields. A Crop Simulation Model (CSM) 116 is a simulation model thathelps estimate crop yield or crop heights or crop growth as a functionof weather conditions, soil conditions, and/or choice of crop managementpractices. Crop simulation models 116 can been classified into threebroad categories.

(1) Statistical models: These typically rely on yield information forlarge areas (across a county) and identify broad trends. The two maintrends identified are a secular trend of a gradual increase in cropyield and variation based on weather conditions.

(2) Mechanistic models: These attempt to use fundamental mechanisms ofplant and soil processes to simulate specific outcomes. These involvefairly detailed and computation-intensive simulations.

(3) Functional models: These use simplified closed functional forms tosimulate complex processes. They are computationally easier thanmechanistic models, and can often give results that are of somewhatlesser accuracy.

Crop simulation models 116 use quantitative descriptions ofeco-physiological processes to predict plant growth and development asinfluenced by environmental conditions and crop management, which arespecified for the model as input data. Crop modeling 116 can be beenused primarily as a decision-making tool for crop management.

Of the two different, but not exclusive, approaches to crop modeling,one focuses on dissected parts of plants, like leaf canopy or rootstructure. The other approach integrates models for differentphysiological traits to simulate the growth of the whole plant.Whole-crop computer simulation models use plant physiology andenvironmental variables to calculate plant growth, or more specifically,yield and dry matter production. Both directions are increasinglycoupled with molecular genetics to facilitate crop modeling.

Physiology-based crop simulation models have become a key tool inextrapolating the impact of climate change from limited experimentalevidence to broader climatic zones, soil types, crop managementregimens, crops and climate change scenarios. While these models are asimplification of the reality, they allow a first assessment of thecomplexity of climate change impact in agriculture. The crop models 116play an increasingly important role in assisting agriculture to adapt toclimate change. This includes the use of modeling to optimize managementpractices, assist in breeding programs, develop new crop rotations, andmaximize the value of seasonal climate forecasts. In order to meet theincreasing demand for assessment of climate change impact, crop modelsneed to be further improved and tested with climate change scenariosinvolving various changes in ambient temperature and carbon dioxide(CO₂) concentration. It is contemplated that the crop models 116 of thepresent invention can be any type of crop model incorporating any typeof variables or parameters. Thus, these crop simulation models 116 canbe employed to determine the short-term impact of weather on growth anddevelopment as well as the long-term impact of climate and associatedenvironmental risks on crop yield or crop growth.

FIG. 5 is a block/flow diagram of exemplary system for integrating cropmodels, maps derived from SAR, earth observations data processing, andin-situ observation data to a portal capable of being accessed byend-users, in accordance with an embodiment of the present invention.

In various exemplary embodiments, the earth observation data 130 and thein-situ observations data 150 can be provided to a geo portal 90including web services for SAR data access, analysis, and distribution.The geo portal 90 communicates with the crop growth modeling 140 derivedfrom the crop models 116 to derive data based on the fields of crops132. The in-situ observation data 150 can be provided, in one example,by farmers 152 monitoring their crops via a mobile device 154.

Regarding crop modeling 140, crop models are computer models, which aremathematical representations of a real world system. The main goals ofthe crop models 116 is to estimate agricultural production as a functionof weather, soil conditions, and crop management. Crop models 116 canuse one or more sets of differential equations, and calculate both rateand state variables over time, normally from planting until harvestmaturity or final harvest. In addition to the types of models alreadydescribed, some further models can include:

Dynamic Crop Simulation Models: These models predict the changes in cropstatus with time as a function of exogenous parameters. For example,models that predict the changing number of bolls on a cotton plantthroughout the growing season, or the changing soil water content ortemperature at a certain depth throughout the season, are dynamicsimulation models.

Phenological Models: Are the broad classes of models that predict thecrop development from one growth stage to another. These predictions aregenerally based upon the accumulated heat units, with development beingdelayed by various stresses.

Stochastic Models: Are those that are based upon the probability ofoccurrence of some events or exogenous variables. They can havemechanistic sub models or subroutines. Weather variables are oftentreated in a stochastic manner or probability of occurrence and as suchcan be combined with a mechanistic crop model. The same might also bedone with insects, diseases, and weeds.

Physically and Physiologically Based Simulation Models: Are thosemechanistic models whose plant or soil processes can be physiologically,physically or chemically described. For example, nitrogen can be takenup from the soil by root systems based upon the soil nitrogen contentand the rate of solution flow to the root. Thus the physical placementof the fertilizer with regard to the plant root system is important, aswell as soil and plant nitrogen transformation.

Moreover, data is needed to run the crop simulation model 116. All cropsimulation models 116 receive as input, data on the management of thecrop 132, as well as the macro and micro environmental factorsassociated with the weather and the soil. Management data includes alatitude of a site, row spacing, plant population, amount and timing offertilizer applications, and similar information. In particular,meteorological data can include max time (Tmax) and min time (Tmin),total solar radiation, precipitation, humidity, and wind speed. Soildata can include depths of the major soil horizons, for each horizon,the particle size analysis, bulk density (BD), water release curve andsaturated hydrocarbons (HC), residual fertilizer content at the start ofthe season, organic matter content at planting, and soil temperature.Additional data can include crop management data and crop coefficients.

Therefore, SAR provides for all-weather, day or night imagingcapabilities and frequent revisits, thus being a dominanthigh-resolution remote sensing data source for agriculture monitoring intropical and subtropical regions. SAR data not only provides data whenoptical remote sensing data is not possible to acquire, but alsoprovides information about geometric structure and moisture content ofthe vegetation, which is needed by agronomists and other end-users. SARdata can be enhanced by further in-situ observation data 150 provided byand to end-users (e.g., farmers 152).

The system in FIG. 5 can further provide information to regionalauthorities for the implementation of agro-environmental policies likewater and fertilizer management, drive land use management, supportfarming activities, with particular focus on the development ofsustainable management practices, and provide independent reliableinformation to the agri-business sector. The availability of informationon crop conditions in space and time is an important issue to supportthe development of more productive and sustainable farming systems. Thisavailability also contributes to increase competitiveness and to faceenvironmental challenges, such as reduction of greenhouse gas emissionand water consumption, soil contamination, and degradation. Thus, thereis a recognized need to develop, validate, and apply methods that canoperationally provide information regarding crops development or growthmonitoring, height difference determination, crop field comparison,yield forecasting, and crop height estimation, early warning onnutritional and water stress and on biotic and abiotic risks. Thedevelopment of these methods, requires the application of suitable SARtechniques able to provide cost effective and high quality spatializedinformation on the agro-ecosystems. Geo-spatial products andgeo-information useful for crop monitoring and management can nowadaysbe efficiently produced via SAR and crop modelling systems for bothregional and local applications.

FIG. 6 is a block/flow diagram illustrating monitoring of crops viasatellites generating SAR images and sending the SAR images forprocessing, in accordance with an embodiment of the present invention.

In various exemplary embodiments, at time T=0, there may be only a bareland image 160. At time T=1, there may be a seed image 162. At time T=2there may be a small plant image 164. At time T=3 there may be a fullygrown plant image 166. The images 160, 162, 164, 166 can be generated bythe satellite 12 having SAR capabilities and transmitted to, e.g., afirst database 170 and/or a second database 172. The first database 170can, in one example, collect images of bare land. The second database172 can collect images from the seeding phase to the fully grown phase.One skilled in the art can contemplate a plurality of differentdatabases for storing and classifying such images. The images in thedatabases 170, 172 can be processed by a computing device 174. Thecomputing device 174 can communicate with, e.g., a historical cropplanting database 178 to compare the generated images to pre-storedimages. The computing device 174 can also generate differential phaseimages 176. These images can be used to compare a crop or a field ofcrops from a starting point (e.g., planting) to an ending point (e.g.,fully grown plant). All this data can be transmitted to a smartphone orlaptop 154 or any other computing device handled or owned or operated byan end-user 152 (e.g., a farmer or land owner).

The SAR images can also be supplemented with at least topography data,optical images, surveys, and farmer assessments to verify and improveestimations of the height of the crops. For example, the SAR images canbe used to determine a type of crop and a land plot boundary can becreated that a farmer reviews, evaluates, confirms or verifies throughthe use of an electronic device (e.g., mobile device) in order tofurther improve the estimates of the heights of crops.

Moreover, all the SAR data for a certain crop sharing a same growthrate, height or texture can be collected or aggregated so that thesystem can estimate all planted lands that are growing a certain type ofcrop. Also, from the crop growth rate, the system can estimate how wellan area or plot of land is performing compared with another geographicalregion and the system can correlate plant growth rates with an amount ofprecipitation that falls within that area or plot of land, averagetemperatures and dew points, how many extreme events (low temperature,high temperature, frost, etc.) occurred, and how they correlate withgrowth rate.

Therefore, growth rates of a crop or of a field of crops or of multiplefields of crops can be monitored, SAR images generated by satellites 12,SAR image data stored in databases 170, 172, SAR image data processed bycomputing devices 174, and SAR image data received by end-users 152 viatheir electronic devices 154.

FIG. 7 is a block/flow diagram illustrating monitoring different cropsfrom different fields via satellites generating SAR images forprocessing, in accordance with an embodiment of the present invention.

In various exemplary embodiments, a first field 180 is shown where thereis bare land, a second field 182 is shown where a first plant 190 hasgrown (e.g., soybean), a third field 184 is shown where a second plant192 has grown (e.g., corn), and a fourth field 186 is shown where thesecond plant 192 has grown (e.g., corn). Satellites 12 can collect SARdata from each of the fields 180, 182, 184, 186. The SAR data can betransmitted from the satellites 12 to one or more databases 172 forfurther processing.

The change in height of a crop can be detected in each field 180, 182,184, 186. In the bare field 180, there is no change in height. In fact,there is no plant whatsoever detected. In case no change is detected,the lack of signal is associated with either: 1) no seed was put intothe ground or 2) seed was planted but never emerged. If no change isdetected during, e.g., a 2 month period, that area is classified as bareland 180.

In the second field 182, soybean crops are detected, whereas in thesecond and third fields 184, 186 corn crops are detected. Higher growthrates are associated with corn, and lower growth rates can be associatedwith soybean. Also, similarities and differences between the third andfourth fields 184, 186 for the same crop (e.g., corn) can be calculatedor determined. For example, the crops in fields 184 and 186 may havegrowth to a similar height because they were exposed to the sameconditions, even though the fields 184 and 186 may have been indifferent counties or different states. In the alternative, the crops infields 184 and 186 may have growth to a totally different height for thesame crop, even though the fields were in the same county and within adistance of 10 miles from each other. The crop models 116 can determinethat some other factor was accountable for such distinct variation. Thisdata can be provided to farmers 152 to make adjustments to next year'sharvest.

The growth rate can be confirmed from the crop models 116 (FIG. 4),which takes into account the soil, weather, and crop type, anddetermines the growth rate for that particular region. If the growthrate is confirmed by the SAR extracted from the crop height growth rate,then that particular area is classified to a particular crop. The cropmodel 116 can create a table with growth rates for a particular cropbased on planting date and environmental information. These crop models116 can be improved by integration of the vegetation index extractedfrom the images generated by the satellite 12 having SAR capabilities.

The growth rate (change in height) can be used as one parameter orvariable for a decision tree classifier where the change in rateindicates specific crops. Further, the signal strength and the specificspeckle noise can be used to improve the classification. The historicalcrop planting data is employed where the speckle noise is quantifiedacross farms where well known crops have been planted. One way toquantify the speckle noise is to delineate each farm/field and then usethe SAR signal from that farm/field and calculate the semivariogram ofthe noise. Each crop will have a specific variation associated with theplanting pattern, distance between rows, and the canopy structure. Thesevariabilities are expected to be similar from farm to farm or field tofield (e.g., in the third and fourth fields 184, 186). Similarly thecross correlation between signals from the same farm/field acquired attwo different moments of time can be used to calculate local disorder asan indicator of crop development.

FIG. 8 is a block/flow diagram illustrating estimation of rate of growthfor different crops in different fields, in accordance with anembodiment of the present invention.

In various exemplary embodiments, the crop models 116 can be used toestimate a crop height at the end of the season (or at harvest time).For instance, at time T=0, a seed 190 is detected. T=0 can correlate tothe time of planting or seeding. At time T=1, a plant 192 might havegrown to a first height. This can be, e.g., after a week. At time T=2,the plant 194 grew a bit bigger to a second height. This can be, e.g.,after 3 weeks. The difference in heights between time periods T=1 andT=2 can be designated as H1. At T=3, the plant 196 has grown evenfurther to a third height. This can be, e.g., after 6 weeks. Thedifference in heights between time periods T=3 and T=2 can be designatedas H2. At T=4, the plant 198 has grown even further to a fourth height.This can be, e.g., after 8 weeks. The difference in heights between timeperiods T=4 and T=3 can be designated as H3. The crop models 116 can beused to estimate the crop growth at time T=5, which can be the harvesttime. The estimated plant 200 is shown at T=5. The plant 200 can beestimated based on a number of factors or parameters or variablesdiscussed above. The continuously collected data can be provided to acomputing device 174 for further processing and for executing the cropmodels 116 (FIG. 4). Thus, the growth rate for a crop during a growingseason can be calculated based on the generated SAR images received fromsatellites 12 and crop development during the growing season can becalculated.

Therefore, according to the present invention, the crop type can bedetermined, the SAR images can be distinguished and classified, and avegetation signal between different crops using optical imaging and SARcapable satellites can be determined. Moreover, bare land can bedetermined based on signal strength and a growth rate can be calculatedfor each crop based on signal growth during the growing season.Similarities and differences between farms or lands or fields having thesame crop planted can also be determined. The total area (within acounty or state or any region) that has similar crops planted (e.g.,corn) can further be detected, as well as the crop development duringthe growing season and the soil moisture based on background signallevel. The SAR data can also be used to assess potential damages to thecrops and relay that information to the end-users (e.g., farmers and/orlandowners).

In summary, for farmers and land managers, increasing crop yields andcutting costs while reducing environmental pollution is a constantchallenge. To accomplish this goal, many farm managers are looking fornew technologies to help them decide when and where to irrigate,fertilize, seed crops, and use herbicides. Currently the decisions arebased on very limited data collected in “spot checks” from the ground.However, recent technological advances in geographic information systems(GIS) and computer modeling are playing a part in farm management andprecision farming. Using SAR data collected by satellites, importantagricultural factors like plant health, plant cover and soil moisturecan be monitored from space, providing a much bigger picture of the landsurface that can be combined with other technologies to help cut costs,increase crop yields, estimate heights of crops, and compare heights ofcrops across different fields in different areas or regions of a countyor state or from country to country.

Indeed, SAR is the premier sensor for the detection of crop growthchanges and crop height changes because it is sensitive to small surfacechanges on the order of the radar wavelength (1 m down to severalmillimeters). SAR is also independent of solar illumination and isgenerally unaffected by cloud cover. In addition, SAR has the advantageof providing control over such factors as power, frequency, phase,polarization, incident angle, spatial resolution, and swath width, allof which are important when designing and operating a system for theextraction of quantitative information.

The descriptions of the various embodiments of the present inventionhave been presented for purposes of illustration, but are not intendedto be exhaustive or limited to the embodiments described. Manymodifications and variations will be apparent to those of ordinaryskills in the art without departing from the scope and spirit of thedescribed embodiments. The terminology used herein was chosen to bestexplain the one or more embodiments, the practical application ortechnical improvement over technologies found in the marketplace, or toenable others of ordinary skills in the art to understand theembodiments described herein.

The present invention can be a system, a method, and/or a computerprogram product. The computer program product can include a computerreadable storage medium (or media) having computer readable programinstructions thereon for causing a processor to carry out aspects of thepresent invention.

The computer readable storage medium can be a tangible device that canretain and store instructions for use by an instruction executiondevice. The computer readable storage medium can be, for example, but isnot limited to, an electronic storage device, a magnetic storage device,an optical storage device, an electromagnetic storage device, asemiconductor storage device, or any suitable combination of theforegoing. A non-exhaustive list of more specific examples of thecomputer readable storage medium includes the following: a portablecomputer diskette, a hard disk, a random access memory (RAM), aread-only memory (ROM), an erasable programmable read-only memory (EPROMor Flash memory), a static random access memory (SRAM), a portablecompact disc read-only memory (CD-ROM), a digital versatile disk (DVD),a memory stick, a floppy disk, a mechanically encoded device such aspunch-cards or raised structures in a groove having instructionsrecorded thereon, and any suitable combination of the foregoing. Acomputer readable storage medium, as used herein, is not to be construedas being transitory signals per se, such as radio waves or other freelypropagating electromagnetic waves, electromagnetic waves propagatingthrough a waveguide or other transmission media (e.g., light pulsespassing through a fiber-optic cable), or electrical signals transmittedthrough a wire.

Computer readable program instructions described herein can bedownloaded to respective computing/processing devices from a computerreadable storage medium or to an external computer or external storagedevice via a network, for example, the Internet, a local area network, awide area network and/or a wireless network. The network can includecopper transmission cables, optical transmission fibers, wirelesstransmission, routers, firewalls, switches, gateway computers and/oredge servers. A network adapter card or network interface in eachcomputing/processing device receives computer readable programinstructions from the network and forwards the computer readable programinstructions for storage in a computer readable storage medium withinthe respective computing/processing device.

Computer readable program instructions for carrying out operations ofthe present invention can be assembler instructions,instruction-set-architecture (ISA) instructions, machine instructions,machine dependent instructions, microcode, firmware instructions,state-setting data, or either source code or object code written in anycombination of one or more programming languages, including an objectoriented programming language such as Smalltalk, C++ or the like, andconventional procedural programming languages, such as the “C”programming language or similar programming languages. The computerreadable program instructions can execute entirely on the user'scomputer, partly on the user's computer, as a stand-alone softwarepackage, partly on the user's computer and partly on a remote computeror entirely on the remote computer or server. In the latter scenario,the remote computer can be connected to the user's computer through anytype of network, including a local area network (LAN) or a wide areanetwork (WAN), or the connection can be made to an external computer(for example, through the Internet using an Internet Service Provider).In some embodiments, electronic circuitry including, for example,programmable logic circuitry, field-programmable gate arrays (FPGA), orprogrammable logic arrays (PLA) can execute the computer readableprogram instructions by utilizing state information of the computerreadable program instructions to personalize the electronic circuitry,in order to perform aspects of the present invention.

Aspects of the present invention are described herein with reference toflowchart illustrations and/or block diagrams of methods, apparatus(systems), and computer program products according to embodiments of theinvention. It will be understood that each block of the flowchartillustrations and/or block diagrams, and combinations of blocks in theflowchart illustrations and/or block diagrams, can be implemented bycomputer readable program instructions.

These computer readable program instructions can be provided to at leastone processor of a general purpose computer, special purpose computer,or other programmable data processing apparatus to produce a machine,such that the instructions, which execute via the processor of thecomputer or other programmable data processing apparatus, create meansfor implementing the functions/acts specified in the flowchart and/orblock diagram block or blocks or modules. These computer readableprogram instructions can also be stored in a computer readable storagemedium that can direct a computer, a programmable data processingapparatus, and/or other devices to function in a particular manner, suchthat the computer readable storage medium having instructions storedtherein includes an article of manufacture including instructions whichimplement aspects of the function/act specified in the flowchart and/orblock diagram block or blocks or modules.

The computer readable program instructions can also be loaded onto acomputer, other programmable data processing apparatus, or other deviceto cause a series of operational blocks/steps to be performed on thecomputer, other programmable apparatus or other device to produce acomputer implemented process, such that the instructions which executeon the computer, other programmable apparatus, or other device implementthe functions/acts specified in the flowchart and/or block diagram blockor blocks or modules.

The flowchart and block diagrams in the Figures illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods, and computer program products according to variousembodiments of the present invention. In this regard, each block in theflowchart or block diagrams may represent a module, segment, or portionof instructions, which includes one or more executable instructions forimplementing the specified logical function(s). In some alternativeimplementations, the functions noted in the blocks may occur out of theorder noted in the figures. For example, two blocks shown in successionmay, in fact, be executed substantially concurrently, or the blocks maysometimes be executed in the reverse order, depending upon thefunctionality involved. It will also be noted that each block of theblock diagrams and/or flowchart illustration, and combinations of blocksin the block diagrams and/or flowchart illustration, can be implementedby special purpose hardware-based systems that perform the specifiedfunctions or acts or carry out combinations of special purpose hardwareand computer instructions.

Reference in the specification to “one embodiment” or “an embodiment” ofthe present principles, as well as other variations thereof, means thata particular feature, structure, characteristic, and so forth describedin connection with the embodiment is included in at least one embodimentof the present principles. Thus, the appearances of the phrase “in oneembodiment” or “in an embodiment”, as well any other variations,appearing in various places throughout the specification are notnecessarily all referring to the same embodiment.

It is to be appreciated that the use of any of the following “/”,“and/or”, and “at least one of”, for example, in the cases of “A/B”, “Aand/or B” and “at least one of A and B”, is intended to encompass theselection of the first listed option (A) only, or the selection of thesecond listed option (B) only, or the selection of both options (A andB). As a further example, in the cases of “A, B, and/or C” and “at leastone of A, B, and C”, such phrasing is intended to encompass theselection of the first listed option (A) only, or the selection of thesecond listed option (B) only, or the selection of the third listedoption (C) only, or the selection of the first and the second listedoptions (A and B) only, or the selection of the first and third listedoptions (A and C) only, or the selection of the second and third listedoptions (B and C) only, or the selection of all three options (A and Band C). This may be extended, as readily apparent by one of ordinaryskill in this and related arts, for as many items listed.

Having described preferred embodiments of a system and method forassessing crop development by using synthetic aperture radar (SAR)images (which are intended to be illustrative and not limiting), it isnoted that modifications and variations can be made by persons skilledin the art in light of the above teachings. It is therefore to beunderstood that changes may be made in the particular embodimentsdescribed which are within the scope of the invention as outlined by theappended claims. Having thus described aspects of the invention, withthe details and particularity required by the patent laws, what isclaimed and desired protected by Letters Patent is set forth in theappended claims.

1. A method for assessing crop development by using a synthetic apertureradar (SAR), the method comprising: generating SAR images from scanningfields including crops; monitoring growth of the crops within the fieldsduring a predetermined time period; estimating an average height of thecrops during the predetermined time period by using interferometricinformation from one or more of the SAR images; and identifying croptype and collecting all SAR data related to the identified crop typesharing a same growth rate, height, and texture to estimate all plantedlands growing the identified crop type.
 2. The method of claim 1,further comprising monitoring reflected radar signals from the cropsreceived by the one or more satellites.
 3. The method of claim 2,further comprising extracting at least soil composition data, soilmoisture data, and vegetation density data.
 4. The method of claim 1,further comprising supplementing the SAR images with at least topographydata, optical images, surveys, and farmer assessments to verify andimprove estimations of the height of the crops.
 5. The method of claim1, further comprising differentiating between crops in different fieldsby monitoring changes in the height of the crops.
 6. The method of claim1, further comprising determining bare land images and creating a bareland topology database for estimating the height of the crops.
 7. Themethod of claim 1, further comprising determining average crop heightdifferences between crops in different fields.
 8. The method of claim 1,further comprising comparing crop fields in different regions growing asame crop to determine similarities and differences therebetween and tofurther compare growth performance therebetween by analyzing at leastaverage temperature, dew point, and occurrence of extreme events.
 9. Themethod of claim 1, further comprising determining a total area having asame crop planted.
 10. The method of claim 1, further comprisingassessing potential damage to the crops and relaying such information toone or more end-users.
 11. A system for assessing crop development byusing a synthetic aperture radar (SAR), the system comprising: one ormore moving aerial objects for generating SAR images from scanningfields including crops; a SAR processing system for monitoring growth ofthe crops within the fields during a predetermined time period; and cropgrowth models for estimating an average height of the crops during thepredetermined time period, wherein crop type is identified and SAR datarelated to the identified crop type sharing a same growth rate, height,and texture are collected to estimate all planted lands growing theidentified crop type.
 12. The system of claim 11, wherein reflectedradar signals from the crops received by the one or more moving aerialobjects are monitored
 13. The system of claim 12, wherein at least soilcomposition data, soil moisture data, and vegetation density data areextracted.
 14. The system of claim 11, wherein the SAR images aresupplemented with at least topography data, optical images, surveys, andfarmer assessments to verify and improve estimations of the height ofthe crops.
 15. The system of claim 11, wherein crops in different fieldsare differentiated by monitoring changes in the height of the crops. 16.The system of claim 11, wherein bare land images are determined and abare land topology database is created for estimating the height of thecrops.
 17. The system of claim 11, wherein average crop heightdifferences between crops in different fields are determined.
 18. Thesystem of claim 11, wherein crop fields in different regions growing asame crop are compared to determine similarities and differencestherebetween and growth performance therebetween is compared byanalyzing at least average temperature, dew point, and occurrence ofextreme events.
 19. The system of claim 11, wherein a total area havinga same crop planted is determined.
 20. A system for assessing cropdevelopment by employing a synthetic aperture radar (SAR), the systemcomprising: one or more moving aerial objects for generating SAR imagesfrom scanning fields including structures where a location and a heightof the structures is well-established, wherein a signal reflected fromthe well-established structures and a signal reflected from cropsprovide for a change that quantifies a height of the crops; and a SARprocessing system for measuring an absolute height of the crops at amoment the SAR images are acquired.